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Modelling white matter with spherical deconvolution: How and why?
Author(s) -
Dell'Acqua Flavio,
Tournier J.Donald
Publication year - 2019
Publication title -
nmr in biomedicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.278
H-Index - 114
eISSN - 1099-1492
pISSN - 0952-3480
DOI - 10.1002/nbm.3945
Subject(s) - deconvolution , diffusion mri , tractography , voxel , white matter , orientation (vector space) , realization (probability) , computer science , diffusion imaging , set (abstract data type) , diffusion , field (mathematics) , artificial intelligence , neuroscience , physics , magnetic resonance imaging , psychology , mathematics , algorithm , medicine , statistics , geometry , pure mathematics , radiology , programming language , thermodynamics
Since the realization that diffusion MRI can probe the microstructural organization and orientation of biological tissue in vivo and non‐invasively, a multitude of diffusion imaging methods have been developed and applied to study the living human brain. Diffusion tensor imaging was the first model to be widely adopted in clinical and neuroscience research, but it was also clear from the beginning that it suffered from limitations when mapping complex configurations, such as crossing fibres. In this review, we highlight the main steps that have led the field of diffusion imaging to move from the tensor model to the adoption of diffusion and fibre orientation density functions as a more effective way to describe the complexity of white matter organization within each brain voxel. Among several techniques, spherical deconvolution has emerged today as one of the main approaches to model multiple fibre orientations and for tractography applications. Here we illustrate the main concepts and the reasoning behind this technique, as well as the latest developments in the field. The final part of this review provides practical guidelines and recommendations on how to set up processing and acquisition protocols suitable for spherical deconvolution.